Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

By Reza Azad, Maryam Asadi-Aghbolaghi, Mahmood Fathy, Sergio Escalera. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019

428 citations recorded by the source corpus.

In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f737fccce1c34492ee736f2c455ad6929232c17c

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In Artificial Intelligence

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Citations in the corpus

TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentation (2021, neutral): “2) The multi-scale context approaches, such as BCDU-Net (85.10”
Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization (2020, background): “Figure 1 shows the architecture of original U-Net and Modified U-Net.”
Medical Image Segmentation Review: The Success of U-Net (2022, methodology): “3) Combination of Encoder and Decoder Feature Maps: Another extension of the classic skip connections is introduced in the BCDU-Net by Azad et al. [70] where a bi-directional convolutional long-term-short-term-memory (LSTM) module is added to the skip connections.”
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications (2020, background): “[70] Dense block Vertebral column”
Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentation (2021, methodology): “For data preprocessing, we follow the same procedure as [30].”